Circuit-cutting framework with application for optimization

Quantum optimization problems often require large, complex circuits that exceed the capabilities of current quantum hardware. Circuit cutting, a technique that partitions large quantum circuits into smaller, manageable sub-circuits, presents a promising solution for this limitation. The state of the art presents different alternatives for wire cutting and gate cutting. The former involves state preparation to initialize the subcircuits and a sequential execution of the subcircuits, while the latter offers a parallel execution of different experiments for each gate to be cut.

We present an innovative application of circuit-cutting techniques to a large-scale quantum optimization problem, showcasing significant improvements in computational efficiency and accuracy. By leveraging classical postprocessing, we achieve near-optimal solutions with reduced quantum resource requirements. Our results highlight the potential of circuit cutting to extend the reach of quantum optimization, making it feasible on near-term quantum devices. We provide a detailed analysis of the trade-offs between circuit depth, qubit count, and computational accuracy. Our results indicate that circuit cutting not only makes large-scale quantum optimization feasible on current hardware but also opens new avenues for practical quantum applications in fields such as logistics, finance, and machine learning.